It has been known that the simplest method of detecting an image portion that has been rotated by an unknown amount of degrees (a rotated image) is based upon matching the rotated input image and a plurality of predetermined rotated templates. However, the above described method requires that rotated images degrees are prepared over 360 in the dictionary and the memory storage space is substantial for the dictionary. To reduce the memory storage requirement, the templates are prepared for a total of 90 degrees in stead of 360 degrees, but the detection is performed over the 360-degree areas.
A first prior art reference, Japanese Patent Publication Hei 11-259657 is entitled as “Image Detection Device, Method and Storage Medium.” The publication proposes a matching method of input image data converted in angular coordinates and the stored image data. A specified image has a constant angle after the image is converted into the angular coordinates. The above is true even if the image initially has an unknown angle. Based upon the above characteristics, a single set of image data is stored with respect to a standard object in stead of 360 degrees of the rotated image data.
A second prior art reference, Japanese Patent Publication Hei 2000-163586 is entitled as “Mark Recognition Device.” The publication discloses that a circular object including the chiseled seal mark is scanned, and the center of the circular object is determined in the image data. The digital image is generated by emphasizing the edge portions of the seal mark. Subsequently, the seal mark image is generated by converting the above generated edge digital image into polar coordinates with respect to the detected center. For each direction of the edge lines, the frequency distribution is extracted as a power spectrum or a characteristic value. The extracted characteristic value is compared to the already stored seal mark characteristic values or templates, and the seal marks that have been chiseled on the engine valve or on a circular metallic surface such as a coin are thus recognized.
As described above, the recognition process is performed corresponding to an arbitrarily rotated angle along the circumference. Even if there are various noises, the recognition process is performed at a high precision level without being influenced by the noises. Furthermore, since the recognition process does not include the calculation intensive process, the recognition process is performed at a sufficiently high speed.
A third prior art reference, Japanese Patent Publication Hei 6-274736 is entitled as “Currency Recognition Device.” The surface pattern is scanned from the currency, and the center of the currency portion is detected from the two-dimensional image data. About the currency center, the two-dimensional image is converted into polar coordinates. Furthermore, the frequency distribution of the image data along the circumferential directions is orthogonally converted to obtain a characteristic value or a power spectrum. For example to determine a currency type, the obtained characteristic value is compared to the already stored templates of characteristic values indicative of the front and back surfaces of the various currencies. In the above described method, it is necessary to prepare only the templates of the characteristic values indicative of the front and back surfaces of the various currencies. Even if the size and the material are substantially identical, the currency type is recognized with certainty without correcting the rotational angle.
A fourth prior art reference, Japanese Patent Publication 2001-43365 is entitled as “Still Image Characteristic Extraction Method.” The orthogonal coordinate data of the digital information and the texture information are extracted from a given object in the still image. Assuming a circle circumscribing the object, in the coordinate whose origin is the center of the circle, a predetermined number is re-sampled from 0 to 2π in the radial direction of the circle from the origin so as to convert the orthogonal coordinate data into the polar coordinate data. From the polar coordinate data, the characteristic value is determined for the object. The characteristic value is independent of the object size or the object rotation. In other words, the characteristic value facilitates the matching process without performing the calculation related to rotation.
A fifth prior art reference, Japanese Patent Publication Hei 9-147109 is entitled as “Method and Device for Detecting Predetermined Mark.” To detect a specified mark in an image, a radial polynomial table is prepared in advance based upon the size of the specified mark. In the process of detecting the specified mark, the radial polynomial table is modified based upon the reduction/expansion ratio of the image. The Zennike Moment is determined by the sum of products between the image and the modified radial polynomial table. The above described method allows the fast and precise detection of a specified mark at an arbitrary position, an arbitrary angle and an arbitrary scale.
A sixth prior art reference, Japanese Patent Publication Hei 9-179982 is entitled as “Predetermined Mark Detection Method.” In detecting a specified pattern using the rotationally independent multidimensional characteristic value, the characteristic value at each dimension must be rotationally independent, and the characteristic value must be determined based upon the central angle. By changing a standard for the central angle determination for each dimension, the image information is minimally lost, and the image is detected or recognized at a high precision level.
On the other hand, the following method is known to detect a circular specified image in an input image. A seventh prior art reference, Japanese Patent Publication Hei 11-110562 is entitled as “Pattern Recognition Method, Device and Recording Medium.” The presence of edges is determined from multiple positions towards the center of the input image. When the edge has been detected from multiple positions, it is determined that an image having an outer circular shape is detected in the input image.
Furthermore, a eighth prior art reference, Japanese Patent Publication 2001-31805 is entitled as “Upper Semi Circular Image Detection Method.” In the input image, a pattern is searched to match initial search conditions. It is determined based upon the dictionary whether or not the distance between the middle point to either a right or left edge is within the prescribed distance from an upper edge in a sub-scanning direction in the searched pattern. If the number of lines beyond the prescribed distance is below a predetermined threshold value and the search reaches a predetermined radius value, proper detection is accomplished despite an improper position due to an edge loss or noise by assuming that a semicircle is detected.
Another image pattern matching method includes the following ninth prior art, Japanese Patent Publication Hei 5-250475, entitled as “Pattern Matching Method.” After the position of the standard digital image pattern is corrected to coincide at a target pattern, unmatched pixels between the standard and target patterns are extracted. The sum of the products is determined by multiplying the extracted pixels by coefficients proportionally indicative of the characteristics of the standard pattern. The above obtained sum is used to determine the amount of match. The above described method provides an accurate evaluation on the match level between the target and standard patterns.
Despite the above described prior art techniques, there remain undesirable features. For example, the first prior art technique requires that as it gets closer to the central portion in an original image, the polar coordinate converted image is more stretched. The amount of information for a unit area is less as it gets closer to the center coordinates. For example, four pixels around the central coordinate in the perpendicular coordinate system are interpolated and stretched over 360 degrees in the polar coordinate system. However, as you get farther away from the central coordinate, since the number of pixels increases at the equidistance from the central coordinate, the amount of information differs between the near-circumferential area and the near-central area. For this reason, when a simple comparison is made among polar coordinate images, a false determination is likely to occur within small radial regions due to errors. To reduce the false determination, at least a normalization process is necessary.
In the second, third and four prior art references, since the polar coordinate image is further compressed into a characteristic value, the precision is lower than the straight comparison of the images. From the point of preventing a false detection, the comparison of the characteristic value is not desirable.
In the fifth and sixth prior art references, if the information such as a number of dimensions is increased to obtain the same level of functionality as the matching of the unprocessed images, it is not common that the dictionary needs to hold more information than the templates over 360 degrees.
The seventh and eighth prior art references illustrate exemplary techniques of determining a matching position for extracting an image. Although the ninth and tenth prior art references is related to a technique of extracting a predetermined pattern, neither of the prior art references is related to a technique of handing a rotated image.
In view of the above issues, there remains some improvement in detecting a predetermined mark with an unknown amount of rotation in an input image. The improvement lies in a limited size of the template or dictionary and a limited number of false detections. The improvements are implemented in a mark detection dictionary generation device, a mark detection device, a mark recognition device, a software program to perform the functions of the above devices or a computer-readable storage medium for storing the software program instructions.